Road damage detection is important for road safety and road maintenance planning. Road surface anomalies, like potholes, cracks and ravelling, affect driving conditions, such as driving comfort and safety, noise emission, load loss of trucks, increase of fuel consumption and traffic circulation. Locali- sation of these anomalies allows for targeted road maintenance, which contributes to the improvement of driver safety, comfort and the optimisation of road maintenance. The current technique to detect road damage is that road inspectors determine road damage in road images. However, the results are susceptible to human subjectivity. An improvement on image based road damage detection is using LiDAR data, because the geometry of road damage is measured too. To mitigate the issue of human subjectivity, an automated method for road damage detection was developed for the profile laser scanner on the IV-Infra car. This laser scanner is mounted at the back of the vehicle so that its profile lines are perpendicular to the driving direction. The proposed method consists of: (I) feature extraction with a sliding window algorithm; (II) K-means clustering to create training data; (III) Random Forest classification and (IV) morphological operations to remove noise and identify larger damage patches. This method was tested on an 800-meter long provincial road with different road defects and road types. Most occurring road damages are cracks, craquel and raveling. The results of this method were validated in two ways: using a road inspectors damage classification and a custom-made validation set based on orthophotos. An overall accuracy of 73% is achieved for the fully automated process. When training of the Random Forest was based on an improved, semi-automated training data, the overall accuracy was 58%, this gives visual clear results. This is explained by more noise are presented in the results based on the fully automatic method, which is overlapped with the coarse road inspector’s data. Optical inspection shows that the semi-automated method identified almost all damages of the custom-made validation set, although a shift between the point cloud and the validation is found. Still, the method has some difficulties with detecting the transverse cracks. This problem can be solved by integrating the two other mounted laser scanners of the Iv-Car, but pre-processing is needed to organise the point cloud. Also, an improvement in georeferencing the validation data would help to optimise the method and training data. Nevertheless, promising results are achieved by this method.